Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …

Image-to-image regression is an important learning task, used frequently in biological
imaging. Current algorithms, however, do not generally offer statistical guarantees that …

Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …

When deploying machine learning models in high-stakes robotics applications, the ability to
detect unsafe situations is crucial. Early warning systems can provide alerts when an unsafe …

We study fair machine learning (ML) under predictive uncertainty to enable reliable and
trustworthy decision-making. The seminal work of'equalized coverage'proposed an …

Accurate probabilistic predictions are essential for optimal decision making. While neural
network miscalibration has been studied primarily in classification, we investigate this in the …

Conditional validity and length efficiency are two crucial aspects of conformal prediction
(CP). Achieving conditional validity ensures accurate uncertainty quantification for data …

Conformal inference is a popular tool for constructing prediction intervals. We consider here
the scenario of post-selection/selective conformal inference, that is, prediction intervals are …

To provide rigorous uncertainty quantification for online learning models, we develop a
framework for constructing uncertainty sets that provably control risk--such as coverage of …

We introduce a method for online conformal prediction with decaying step sizes. Like
previous methods, ours possesses a retrospective guarantee of coverage for arbitrary …